A conventional mode of procedure in data reduction for the purpose of the transmission of digital images or digital sequences is the subdivision of image regions into smaller regions, so-called blocks (FIG. 2). These blocks are typically 8--8 or 16--16 pixels (picture elements) in size (FIG. 3). Examples of such block-related processes for image coding are the so-called transformation processes, of which the most frequently applied representative is the so-called discrete cosine transformation (D.C.T.). Signal flow diagrams for coding processes of this type for individual images or image sequences are represented in FIGS. 1a and 1b.
The purpose of coding is data reduction, by means of which digital transmission of images or image sequences only becomes possible in many instances. A known principle in this regard is the so-called redundancy reduction, in which statistic characteristics of the image signals are utilized for data reduction. The sole use of redundancy reduction guarantees the complete reconstructability of the image information from the coded data. However, with the aid of redundancy reduction alone it is normally possible to achieve only a data compression by factors of 2 to 3. In most applications of image coding and image sequence coding, however, there is a need for higher compression factors, for example at least a compression factor of 7 in the case of high definition television (HDTV). In order to achieve such compression factors and higher, such as in the case of videophone, it is also necessary to use methods of irrelevance reduction. However, in this case the original signal information is partially lost and image transmission errors are produced. The aim is, however, for these image errors not to be visible to a viewer or to disturb as little as possible.
In a transformation coding, redundancy reduction and irrelevancy reduction are generally applied jointly to the signal values in the transformation region of an image block (A. N. Netravali, W. G. Haskell: "Digital pictures, representation and compression" Plenum Press, New York, 1988). In DCT, these are the so-called DCT coefficients. Typical modes of procedure in irrelevance reduction are, on the one hand, simply to leave out entire subsets of DCT coefficients, and on the other hand to quantize the remaining, significant coefficients more coarsely, that is to say to represent them in a coarser raster of values than originally assigned to them. A raster of values can be characterized by a parameter Q for its fineness. The larger the Q, the coarser the raster. In a so-called linear quantization, this parameter is simply the quantization step width, that is to say the uniform interval between two adjacent raster values. Different types of quantization of DCT coefficients are known. In some processes, the individual coefficients are quantized differently in terms of fineness. The different finenesses are derived in this case from assumptions concerning the visual significance of the coefficients. Such a process is described, for example, in D. McLaren, D. T. Nguyen: "Video bitrate reduction through psychovisual impression", Proc. 1990 Video Communications Workshop, Melbourne, 9 to 11 Jul. 1990. In another process, all coefficients of equal fineness are quantized, as is described, for example, in W. Tengler, W. Ja.beta.: "Interlaced and Progressive HDTV-Coding, a Comparison for 140 Mbit/s- Transmission" Proceedings 1990 Austr. Video Communication Workshop Melbourne, 9 -11 Jul. 1990.
Different types of quantization generate different types of image errors. In the case of a transformation such as DCT, however, these image errors are generally distributed in each case over an entire block in the image region, irrespective of the quantization scheme employed. Whether these errors are either not visible at all or are more or less disturbing visually, depends on the original image content, on the one hand, and on the mean amplitude of the errors in a block, on the other hand. This mean error amplitude is directly connected to the previously mentioned quantization parameter Q for a block. The larger Q is set, the smaller the data rate required for coding a block, but also the larger in general the mean error amplitude.
In advanced processes for coding individual images and image sequences, the quantization parameter Q is now adapted per block to the image content in such a way that given the observance of specific limits for the resulting data rate the image errors disturb visually as little as possible. In this case, the image content in a block is analyzed and the quantization parameter Q is set as a function of the result of the analysis. This analysis can be undertaken either on pixel blocks, that is to say on blocks of picture elements, or on blocks of DCT coefficients. Analysis methods which analyze blocks of picture elements are, for example, classified in the publications of McLaren (McLaren 1990) and Tengler (Tengler 1990). However, these publications do not go into the possible defects of the adaptive quantization processes described there, which are improved by means of the process according to the invention.
The general mode of procedure of the analysis methods described in the publications by McLaren and Netravali is to be described briefly below in order to be able for the purpose of describing the present invention to have recourse to the terms and notations thereby introduced. In the analysis methods already known, a so-called activity measure is firstly calculated from the image signal values of all the pixels in a block. The magnitude of this activity measure is directly proportional to the intensity of the mean amplitude fluctuation of the image signal values of all the pixels of the block under consideration. In the article by Tengler and Jasz, use is made, for example, as the activity measure A of the sum of absolute local image signal differences (FIG. 3): ##EQU1## where y(m, n) : image signal value of the pixel with the index m, n,
m: row index, n: column index in the block, and
M,N: block side lengths, for example 8 or 16.
A preliminary parameter P is taken as a function of the activity measure calculated in this way, for example from a table: EQU (2) P=Q.sub.O (A).
The quantization parameter Q actually applied for a block is, finally, obtained from P through multiplication by a scale factor F.sub.Q : EQU (2') Q=F.sub.Q . P
F.sub.Q is a factor which is derived from the occupancy of a data buffer (FIG. 1b) for controlling the observation of rate limitations. An example of a function Q.sub.O (A) is described in the publication by W. Tengler 1990, for example, and represented graphically in FIG. 4.
The adaptive quantization scheme described in the article by W. Tengler leads to a good image quality even in the case of not too strict secondary conditions on the magnitude of the resulting data rate. In the case of specific image contents, however, image errors produced by the quantization can become visible. Typical of such image contents is that a block can contain both regions of high activity, that is to say with fine high-contrast details, and regions of low activity, that is to say with only weakly varying image content.
Setting the quantization parameter is, however, now optimized with respect to the required data rate and with the aim of avoiding image errors on the assumption that the activity in a block is relatively uniformly distributed. The higher the activity, the larger the image errors may then be without becoming disturbingly visible, that is to say the larger the quantization parameter may be set for reducing the data rate. In the type of process assumed here for coding digital images or image sequences, however, the image errors are generally uniformly distributed over a block by the quantization. Consequently, the quantization parameter set for the entire block can lead to image errors which although not becoming disturbingly visible in the parts of the block of higher activity can, however, become so in those of lower activity.